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Chapter 1: Throughput and latency characteristics for services that ingest data
Chapter 9: Implementing appropriate configuration options for batch ingestion
Chapter 11: Setting up schedulers by using EventBridge, Airflow, or time-based schedules for jobs and crawlers
Chapter 15: Implementing throttling and overcoming rate limits (DynamoDB, RDS, Kinesis)
Chapter 16: Managing fan-in and fan-out for streaming data distribution
Chapter 23: Connecting to different data sources (Java Database Connectivity [JDBC], Open Database Connectivity [ODBC])
Chapter 24: Integrating data from multiple sources
Chapter 26: Implementing data transformation services based on requirements ( EMR, Glue, Lambda, Redshift)
Chapter 29: Creating data APIs to make data available to other systems by using services
Chapter 30: How to integrate various services to create ETL pipelines
Chapter 34: Using orchestration services to build workflows for data ETL pipelines (Lambda, EventBridge, Managed Workflows for Airflow [ MWAA], Step Functions, Glue workflows)
Chapter 35: Building data pipelines for performance, availability, scalability, resiliency, and fault tolerance
Chapter 36: Implementing and maintaining serverless workflows
Chapter 38: Continuous integration and continuous delivery (CI/CD) (implementation, testing, and deployment of data pipelines)
Chapter 39: SQL queries (for data source queries and data transformations)
Chapter 42: Data structures and algorithms (graph data structures and tree data structures)
Chapter 44: Optimizing code to reduce runtime for data ingestion and transformation
Chapter 48: Using Git commands to perform actions such as creating, updating, cloning, and branching repositories
Chapter 49: Using the Serverless Application Model ( SAM) to package and deploy serverless data pipelines (Lambda functions, Step Functions, DynamoDB tables)
Chapter 52: Storage services and configurations for specific performance demands
Chapter 53: Data storage formats (.csv, .txt, Parquet)
Chapter 55: How to determine the appropriate storage solution for specific access patterns
Chapter 56: How to manage locks to prevent access to data ( Redshift, RDS)
Chapter 58: Configuring the appropriate storage services for specific access patterns and requirements ( Redshift, EMR, Lake Formation, RDS, DynamoDB)
Chapter 59: Applying storage services to appropriate use cases ( S3)
Chapter 70: Appropriate storage solutions to address hot and cold data requirements
Chapter 72: How to delete data to meet business and legal requirements
Chapter 75: Performing load and unload operations to move data between S3 and Redshift
Chapter 76: Managing S3 Lifecycle policies to change the storage tier of S3 data
Chapter 77: Expiring data when it reaches a specific age by using S3 Lifecycle policies
Chapter 80: How to ensure accuracy and trustworthiness of data by using data lineage
Chapter 87: Establishing data lineage by using AWS tools ( SageMaker ML Lineage Tracking)
Chapter 90: Which services accept scripting ( EMR, Redshift, AWS Glue)
Chapter 94: Using the features of AWS services to process data ( EMR, Redshift, AWS Glue)
Chapter 101: SQL queries (SELECT statements with multiple qualifiers or JOIN clauses)
Chapter 105: Visualizing data by using AWS services and tools (AWS Glue DataBrew, QuickSight)
Chapter 106: Verifying and cleaning data (Lambda, Athena, QuickSight, Jupyter Notebooks, SageMaker Data Wrangler)
Chapter 110: Best practices for performance tuning
Chapter 113: Extracting logs for audits
Chapter 114: Deploying logging and monitoring solutions to facilitate auditing and traceability
Chapter 119: Using CloudWatch Logs to log application data (with a focus on configuration and automation)
Chapter 120: Analyzing logs with AWS services (Athena, EMR, OpenSearch Service, CloudWatch Logs Insights, big data application logs)
Chapter 126: Defining data quality rules (AWS Glue DataBrew)
Chapter 129: Differences between managed services and unmanaged services
Chapter 130: Authentication methods (password-based, certificate-based, and role-based)
Chapter 132: Updating VPC security groups
Chapter 135: Setting up IAM roles for access (Lambda, API Gateway, AWS CLI, CloudFormation)
Chapter 138: Principle of least privilege as it applies to AWS security
Chapter 140: Methods to protect data from unauthorized access across services
Chapter 144: Managing permissions through Lake Formation (for Redshift, EMR, Athena, and S3)
Chapter 145: Data encryption options available in AWS analytics services ( Redshift, EMR, AWS Glue)
Chapter 146: Differences between client-side encryption and server-side encryption
Chapter 149: Applying data masking and anonymization according to compliance laws or company policies
Chapter 150: Using encryption keys to encrypt or decrypt data (AWS Key Management Service [AWS KMS])
Chapter 153: How to log application data
Chapter 158: Using AWS CloudTrail Lake for centralized logging queries
Chapter 160: Integrating various AWS services to perform logging ( EMR in cases of large volumes of log data)
Chapter 161: How to protect personally identifiable information (PII)
Chapter 164: Implementing PII identification (Macie with Lake Formation)
Chapter 165: Implementing data privacy strategies to prevent backups or replications of data to disallowed AWS Regions

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